rec_rate_mareymap
KN
2021-05_10
R Markdown
https://github.com/aursiber/MareyMap
install.packages(MareyMap) install.packages(tcltk) install.packages(tkrplot) install.packages(tools)
## Warning: running command ''/usr/bin/otool' -L '/Library/Frameworks/R.framework/
## Resources/library/tcltk/libs//tcltk.so'' had status 1##
## Attaching package: 'MareyMap'## The following objects are masked from 'package:stats':
##
## df, setNames## Loading required package: viridisLite##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union## mkr set map phys gen vld slidingwindow.3 slidingwindow
## 1 41199 vcar LR999924.1 377201 1.111 TRUE 0.76 0.00
## 2 41202 vcar LR999924.1 406369 1.111 TRUE 0.76 0.00
## 3 41230 vcar LR999924.1 702834 1.111 TRUE 1.08 0.00
## 4 41246 vcar LR999924.1 942878 1.111 TRUE 1.44 1.89
## 5 41256 vcar LR999924.1 943232 1.111 TRUE 1.44 1.89
## 6 41276 vcar LR999924.1 1080480 1.111 TRUE 1.58 1.79
## slidingwindow.2 slidingwindow.4 vld.1 loess
## 1 0.00 0.76 TRUE NA
## 2 0.00 0.76 TRUE 1.11
## 3 0.00 1.08 TRUE 1.24
## 4 1.89 1.44 TRUE 1.34
## 5 1.89 1.44 TRUE 1.34
## 6 1.79 1.58 TRUE 1.49## 'data.frame': 1696 obs. of 12 variables:
## $ mkr : int 41199 41202 41230 41246 41256 41276 41304 41335 41356 41363 ...
## $ set : Factor w/ 1 level "vcar": 1 1 1 1 1 1 1 1 1 1 ...
## $ map : chr "LR999924.1" "LR999924.1" "LR999924.1" "LR999924.1" ...
## $ phys : int 377201 406369 702834 942878 943232 1080480 1356020 1413500 1535700 1759540 ...
## $ gen : num 1.11 1.11 1.11 1.11 1.11 ...
## $ vld : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ slidingwindow.3: num 0.76 0.76 1.08 1.44 1.44 1.58 1.55 1.74 1.61 1.27 ...
## $ slidingwindow : num 0 0 0 1.89 1.89 1.79 2.61 2.61 2.59 1.67 ...
## $ slidingwindow.2: num 0 0 0 1.89 1.89 1.79 2.61 2.61 2.59 1.67 ...
## $ slidingwindow.4: num 0.76 0.76 1.08 1.44 1.44 1.58 1.55 1.74 1.61 1.27 ...
## $ vld.1 : logi TRUE TRUE TRUE TRUE TRUE TRUE ...
## $ loess : num NA 1.11 1.24 1.34 1.34 1.49 1.48 1.42 0.58 0.22 ...## [1] "Total length of genome, incl W: 410758454"## Joining by: map## mkr set map phys
## Min. : 19 vcar:1696 Length:1696 Min. : 14622
## 1st Qu.: 9654 Class :character 1st Qu.: 3867315
## Median :20822 Mode :character Median : 7301015
## Mean :20844 Mean : 7337513
## 3rd Qu.:31587 3rd Qu.:10630375
## Max. :42517 Max. :16942500
##
## gen vld slidingwindow.3 slidingwindow
## Min. : 0.00 Mode:logical Min. : 0.000 Min. : 0.000
## 1st Qu.:13.34 TRUE:1696 1st Qu.: 1.675 1st Qu.: 0.000
## Median :24.52 NA's:0 Median : 3.040 Median : 2.460
## Mean :25.03 Mean : 3.500 Mean : 4.305
## 3rd Qu.:36.75 3rd Qu.: 4.875 3rd Qu.: 5.330
## Max. :61.21 Max. :15.520 Max. :283.070
## NA's :5 NA's :43
## slidingwindow.2 slidingwindow.4 vld.1 loess
## Min. : 0.000 Min. : 0.000 Mode :logical Min. :-12.520
## 1st Qu.: 0.000 1st Qu.: 1.675 FALSE:1 1st Qu.: 1.330
## Median : 2.460 Median : 3.040 TRUE :1695 Median : 2.845
## Mean : 4.305 Mean : 3.500 NA's :0 Mean : 3.567
## 3rd Qu.: 5.330 3rd Qu.: 4.875 3rd Qu.: 4.890
## Max. :283.070 Max. :15.520 Max. : 61.320
## NA's :43 NA's :5 NA's :62
## chr_length chr_start genome_pos rel_pos
## Min. : 6166334 Min. : 0 Min. : 377201 Min. : 0.09783
## 1st Qu.:13236624 1st Qu.: 66099924 1st Qu.: 77390899 1st Qu.:28.70873
## Median :14868897 Median :175009806 Median :187510556 Median :51.82503
## Mean :14332339 Mean :178197377 Mean :185534890 Mean :51.00087
## 3rd Qu.:16000791 3rd Qu.:275115552 3rd Qu.:282160487 3rd Qu.:73.57617
## Max. :17040296 Max. :404592120 Max. :410175120 Max. :99.90938
## ## Joining by: map## map loess loess_sd 1mb
## Length:31 Min. :0.000 Min. : 0.000 Min. : 1.844
## Class :character 1st Qu.:2.907 1st Qu.: 2.041 1st Qu.: 2.774
## Mode :character Median :3.395 Median : 2.766 Median : 3.937
## Mean :3.549 Mean : 3.532 Mean : 5.699
## 3rd Qu.:4.149 3rd Qu.: 4.212 3rd Qu.: 4.652
## Max. :7.244 Max. :10.598 Max. :47.964
## 1mb_sd 2mb 2mb_sd no_overlap_1mb
## Min. : 2.277 Min. :1.842 Min. :1.189 Min. : 1.844
## 1st Qu.: 2.793 1st Qu.:3.056 1st Qu.:1.793 1st Qu.: 2.774
## Median : 3.496 Median :3.623 Median :2.218 Median : 3.937
## Mean : 7.362 Mean :3.834 Mean :2.420 Mean : 5.699
## 3rd Qu.: 4.531 3rd Qu.:4.282 3rd Qu.:2.681 3rd Qu.: 4.652
## Max. :56.134 Max. :7.127 Max. :6.578 Max. :47.964
## no_overlap_1mb_sd no_overlap_2mb no_overlap_2mb_sd map_length
## Min. : 2.277 Min. :1.842 Min. :1.189 Min. :20.06
## 1st Qu.: 2.793 1st Qu.:3.056 1st Qu.:1.793 1st Qu.:41.70
## Median : 3.496 Median :3.623 Median :2.218 Median :46.69
## Mean : 7.362 Mean :3.834 Mean :2.420 Mean :44.36
## 3rd Qu.: 4.531 3rd Qu.:4.282 3rd Qu.:2.681 3rd Qu.:49.58
## Max. :56.134 Max. :7.127 Max. :6.578 Max. :61.21
## chr_length chr_start rate markers
## Min. : 6.166 Min. : 0 Min. :1.750 Min. : 10.00
## 1st Qu.:11.515 1st Qu.:121554284 1st Qu.:2.870 1st Qu.: 39.50
## Median :13.916 Median :233874477 Median :3.362 Median : 58.00
## Mean :13.250 Mean :223364757 Mean :3.422 Mean : 54.71
## 3rd Qu.:15.500 3rd Qu.:332593828 3rd Qu.:3.869 3rd Qu.: 68.50
## Max. :17.040 Max. :404592120 Max. :5.341 Max. :114.00
## marker_density
## Min. :1.355
## 1st Qu.:3.131
## Median :4.283
## Mean :3.945
## 3rd Qu.:4.679
## Max. :6.690## [1] "Markers in map: 1696"## [1] "Mean recombination rate: 3.422"## [1] "Recombination rate Z: 2.478"## [1] "Recombination rate mean automomes: 3.454"## [1] "Window based (2Mb):"## [1] "Mean recombination rate (chr based): 3.834"## [1] "sd recombination rate (chr based): 1.197"## [1] "Mean recombination rate (overall windows): 3.5"## [1] "sd (overall windows): 2.52"## [1] "Recombination rate Z: 2.728"## [1] "Recombination rate mean automomes: 3.87"#test
rate_lm <- lm(rec_rate_mean$rate~rec_rate_mean$chr_length + rec_rate_mean$marker_density)
summary(rate_lm)##
## Call:
## lm(formula = rec_rate_mean$rate ~ rec_rate_mean$chr_length +
## rec_rate_mean$marker_density)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.56312 -0.31054 0.01502 0.42921 1.32246
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.97531 0.56414 8.819 1.43e-09 ***
## rec_rate_mean$chr_length -0.16540 0.06065 -2.727 0.0109 *
## rec_rate_mean$marker_density 0.16190 0.15197 1.065 0.2958
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.67 on 28 degrees of freedom
## Multiple R-squared: 0.2481, Adjusted R-squared: 0.1944
## F-statistic: 4.619 on 2 and 28 DF, p-value: 0.01847cor.test(rec_rate_mean$rate, rec_rate_mean$chr_length)##
## Pearson's product-moment correlation
##
## data: rec_rate_mean$rate and rec_rate_mean$chr_length
## t = -2.84, df = 29, p-value = 0.008164
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7043871 -0.1343412
## sample estimates:
## cor
## -0.4664759cor.test(rec_rate_mean$rate, rec_rate_mean$marker_density)##
## Pearson's product-moment correlation
##
## data: rec_rate_mean$rate and rec_rate_mean$marker_density
## t = -1.2139, df = 29, p-value = 0.2346
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5327314 0.1457991
## sample estimates:
## cor
## -0.2199017rate_lm_window <- lm(rec_rate_mean$`2mb`~rec_rate_mean$chr_length + rec_rate_mean$marker_density)
summary(rate_lm_window)##
## Call:
## lm(formula = rec_rate_mean$`2mb` ~ rec_rate_mean$chr_length +
## rec_rate_mean$marker_density)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.07950 -0.48081 0.00692 0.32981 1.77535
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.800647 0.706866 11.036 1.05e-11 ***
## rec_rate_mean$chr_length -0.298769 0.075994 -3.931 0.000505 ***
## rec_rate_mean$marker_density -0.002107 0.190423 -0.011 0.991248
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8395 on 28 degrees of freedom
## Multiple R-squared: 0.5411, Adjusted R-squared: 0.5083
## F-statistic: 16.51 on 2 and 28 DF, p-value: 1.835e-05anova(rate_lm_window)## Analysis of Variance Table
##
## Response: rec_rate_mean$`2mb`
## Df Sum Sq Mean Sq F value Pr(>F)
## rec_rate_mean$chr_length 1 23.2728 23.2728 33.0184 3.631e-06 ***
## rec_rate_mean$marker_density 1 0.0001 0.0001 0.0001 0.9912
## Residuals 28 19.7356 0.7048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1par(mfrow=c(2,2))
plot(rate_lm_window)
cor.test(rec_rate_mean$`2mb`, rec_rate_mean$chr_length)##
## Pearson's product-moment correlation
##
## data: rec_rate_mean$`2mb` and rec_rate_mean$chr_length
## t = -5.8479, df = 29, p-value = 2.42e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.8645890 -0.5156852
## sample estimates:
## cor
## -0.735609cor.test(rec_rate_mean$`2mb`, rec_rate_mean$marker_density)##
## Pearson's product-moment correlation
##
## data: rec_rate_mean$`2mb` and rec_rate_mean$marker_density
## t = -3.4234, df = 29, p-value = 0.001863
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7485300 -0.2248958
## sample estimates:
## cor
## -0.5364838cor.test(rec_rate_mean$loess, rec_rate_mean$chr_length)##
## Pearson's product-moment correlation
##
## data: rec_rate_mean$loess and rec_rate_mean$chr_length
## t = 0.52591, df = 29, p-value = 0.6029
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2663150 0.4365038
## sample estimates:
## cor
## 0.09719722cor.test(rec_rate_mean$loess, rec_rate_mean$marker_density)##
## Pearson's product-moment correlation
##
## data: rec_rate_mean$loess and rec_rate_mean$marker_density
## t = 0.83874, df = 29, p-value = 0.4085
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2120056 0.4819540
## sample estimates:
## cor
## 0.1538954#plot marey maps
ggplot(rec_rate, aes(rec_rate$phys, rec_rate$gen)) +
geom_point() +
facet_wrap(~rec_rate$map, scales="free") +
ylab("cM") +
xlab("Mb") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
#plot recombination rate per Mb (loess)
ggplot(rec_rate, aes(rec_rate$phys, rec_rate$loess)) +
geom_point() +
geom_line() +
facet_wrap(~rec_rate$map, scales = "free") +
ylab("cM/Mb") +
xlab("Mb") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))## Warning: Removed 62 rows containing missing values (geom_point).## Warning: Removed 2 rows containing missing values (geom_path).
#plot recombination rate per Mb (2 Mb window)
ggplot(rec_rate, aes(rec_rate$phys, rec_rate$slidingwindow.3)) +
geom_point() +
geom_line() +
facet_wrap(~rec_rate$map, scales = "free") +
ylab("cM/Mb") +
xlab("Mb") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))## Warning: Removed 5 rows containing missing values (geom_point).
#in one plot
ggplot(rec_rate, aes(rec_rate$phys, log(rec_rate$slidingwindow.3))) +
geom_point() +
geom_line(aes(colour=rec_rate$map)) +
#facet_wrap(~rec_rate$map, scales = "free") +
guides(colour=FALSE) +
ylab("cM/Mb") +
xlab("Mb") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 4 rows containing missing values (geom_path).
#chr length and rate
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$rate)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Correlation rec rate (maplength/chr size) and chromosome size") +
ylab("cM/Mb") +
xlab("Chromosome length (Mbp)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
#chr length and rate
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$loess)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Correlation rec rate (loess) and chromosome size") +
ylab("cM/Mb") +
xlab("Chromosome length (Mbp)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
#chr length and rate
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$`2mb`)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Correlation rec rate (2Mb windows) and chromosome size") +
ylab("cM/Mb") +
xlab("Chromosome length (Mbp)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$marker_density)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Correlation marker density and chromosome size") +
ylab("Marker density") +
xlab("Chromosome length (Mbp)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
ggplot(rec_rate_mean, aes(rec_rate_mean$marker_density, rec_rate_mean$rate)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Correlation marker density and recombination rate") +
ylab("cM/Mb") +
xlab("Marker density (per Mb)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
ggplot(rec_rate_mean, aes(rec_rate_mean$marker_density, rec_rate_mean$`2mb`)) +
geom_point() +
geom_smooth(method = "lm") +
labs(title = "Correlation marker density and recombination rate (window)") +
ylab("cM/Mb") +
xlab("Marker density (per Mb)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
#coorelation differetn measures of rec rate
ggplot(rec_rate_mean, aes(rec_rate_mean$rate, rec_rate_mean$`2mb`)) +
geom_point() +
#geom_smooth(method = "lm") +
labs(title = "Correlation recombination rate maplength/chrsize and window based") +
ylab("cM/Mb (windows)") +
xlab("cM/Mb (maplength/chrsize)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
#correlation different measures of rec rate
ggplot(rec_rate_mean, aes(rec_rate_mean$rate, rec_rate_mean$loess)) +
geom_point() +
#geom_smooth(method = "lm") +
labs(title = "Correlation recombination rate maplength/chrsize and loess") +
ylab("cM/Mb (loess)") +
xlab("cM/Mb (maplength/chrsize)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
#coorelation differetn measures of rec rate
ggplot(rec_rate_mean, aes(rec_rate_mean$loess, rec_rate_mean$`2mb`)) +
geom_point() +
#geom_smooth(method = "lm") +
labs(title = "Correlation recombination rate loess and window based") +
ylab("cM/Mb (window based)") +
xlab("cM/Mb (loess)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
#chr length and rate orange
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$`2mb`)) +
#geom_point() +
geom_smooth(method = "lm", colour="#FAAB36", fill="#FAAB36") +
geom_pointrange(aes(ymin=rec_rate_mean$`2mb`-rec_rate_mean$`2mb_sd`, ymax=rec_rate_mean$`2mb`+rec_rate_mean$`2mb_sd`), size=0.1) +
labs(title = "Correlation rec rate (2Mb windows) and chromosome size") +
ylab("cM/Mb") +
xlab("Chromosome length (Mbp)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 0.2, colour = "grey"),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
#chr length and rate blue
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$`2mb`)) +
#geom_point() +
geom_smooth(method = "lm", colour="#249EA0", fill="#249EA0") +
geom_pointrange(aes(ymin=rec_rate_mean$`2mb`-rec_rate_mean$`2mb_sd`, ymax=rec_rate_mean$`2mb`+rec_rate_mean$`2mb_sd`), size=0.1) +
labs(title = "Correlation rec rate (2Mb windows) and chromosome size") +
ylab("cM/Mb") +
xlab("Chromosome length (Mbp)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 0.2, colour = "grey"),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))
#chr length and rate blue, points only contour
ggplot(rec_rate_mean, aes(rec_rate_mean$chr_length,rec_rate_mean$`2mb`)) +
geom_smooth(method = "lm", colour="#249EA0", fill="#249EA0") +
geom_pointrange(aes(ymin=rec_rate_mean$`2mb`-rec_rate_mean$`2mb_sd`, ymax=rec_rate_mean$`2mb`+rec_rate_mean$`2mb_sd`), size=0.2, shape=21) +
geom_point(size=3,shape=21, fill="white") +
labs(title = "Correlation rec rate (2Mb windows) and chromosome size") +
ylab("cM/Mb") +
xlab("Chromosome length (Mbp)") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 0.2, colour = "grey"),
axis.text = element_text(size = 12),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12),
legend.text = element_text(size = 12))
#saved as pdf
#regional variation
#plot recombination rate per Mb (2 Mb window)
ggplot(rec_rate, aes(rec_rate$rel_pos, rec_rate$slidingwindow.3)) +
geom_point(colour="dark grey") +
#geom_line() +
geom_smooth(method = "loess", colour="#249EA0", fill="#249EA0") +
ylab("cM/Mb") +
xlab("Relative position") +
scale_y_continuous(expand = c(0.01,0.05)) +
scale_x_continuous(expand = c(0.01,0.05)) +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1, colour = "grey"),
axis.text = element_text(size = 20),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
legend.text = element_text(size = 20))## Warning: Removed 5 rows containing non-finite values (stat_smooth).## Warning: Removed 5 rows containing missing values (geom_point).
ggplot(rec_rate, aes((sqrt((rec_rate$rel_pos-50)^2)), rec_rate$slidingwindow.3)) +
geom_point(colour="dark grey") +
#geom_line() +
geom_smooth(method = "loess", colour="#249EA0", fill="#249EA0") +
ylab("cM/Mb") +
xlab("Relative position") +
scale_y_continuous(expand = c(0.01,0.05)) +
scale_x_continuous(expand = c(0.01,0.05)) +
theme(panel.background = element_blank(),
axis.line = element_line(size = 1, colour = "grey"),
axis.text = element_text(size = 20),
axis.title.x = element_text(size = 20),
axis.title.y = element_text(size = 20),
legend.text = element_text(size = 20))## Warning: Removed 5 rows containing non-finite values (stat_smooth).
## Warning: Removed 5 rows containing missing values (geom_point).
#create black and white palett
palette <- rep(c("light grey", "white"), length.out = length(unique(rec_rate$map)))
palette_blue <- rep(c("#a7d8d9", "#d3eded"), length.out = length(unique(rec_rate$map)))
#plot recombination rate per Mb (2 Mb window)
ggplot(rec_rate, aes(rec_rate$genome_pos, rec_rate$slidingwindow.3)) +
geom_rect(aes(xmin=rec_rate$chr_start, xmax=rec_rate$chr_start+rec_rate$chr_length, ymin=0, ymax=16, fill=rec_rate$map, alpha=0.5)) +
geom_point(size=0.4) +
geom_line(size=0.2) +
scale_fill_manual(values = palette_blue) +
guides(fill=FALSE, alpha=FALSE) +
ylab("cM/Mb") +
xlab("Mb") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 0.2, colour = "grey"),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))## Warning: Removed 5 rows containing missing values (geom_point).
ggplot(rec_rate, aes(rec_rate$genome_pos, rec_rate$slidingwindow.3)) +
geom_rect(aes(xmin=rec_rate$chr_start, xmax=rec_rate$chr_start+rec_rate$chr_length, ymin=0, ymax=16, fill=rec_rate$map, alpha=0.5)) +
geom_point(size=0.4) +
#geom_line(size=0.2) +
geom_smooth(method = "loess", span=0.1,colour="#249EA0", fill="#249EA0") +
scale_fill_manual(values = palette_blue) +
guides(fill=FALSE, alpha=FALSE) +
ylab("cM/Mb") +
xlab("Mb") +
theme(panel.background = element_blank(),
axis.line = element_line(size = 0.2, colour = "grey"),
axis.text = element_text(size = 10),
axis.title.x = element_text(size = 10),
axis.title.y = element_text(size = 10),
legend.text = element_text(size = 10))## Warning: Removed 5 rows containing non-finite values (stat_smooth).
## Warning: Removed 5 rows containing missing values (geom_point).
ggplot(rec_rate, aes(rec_rate$genome_pos, rec_rate$slidingwindow.3)) +
annotate(geom = "rect", xmin=rec_rate$chr_start, xmax=rec_rate$chr_start+rec_rate$chr_length, ymin=-0.2, ymax=16, fill="light grey", colour="white") +
geom_point(size=0.4) +
geom_smooth(method = "loess", span=0.1,colour="#249EA0", fill="#249EA0") +
#geom_line(size=0.2) +
#scale_fill_manual() +
#guides(fill=FALSE, alpha=FALSE) +
ylab("cM/Mb") +
xlab("Mb") +
scale_y_continuous(expand = c(0.01,0.05)) +
scale_x_continuous(expand = c(0.01,0.05)) +
theme(panel.background = element_blank(),
axis.line = element_line(size = 0.5, colour = "grey"),
axis.text = element_text(size = 12),
axis.title.x = element_text(size = 12),
axis.title.y = element_text(size = 12),
legend.text = element_text(size = 12))## Warning: Removed 5 rows containing non-finite values (stat_smooth).
## Warning: Removed 5 rows containing missing values (geom_point).